Chen Minghong, Huang Xiumei, Wu Yinger, Song Shijie, Qi Xianjun
School of Information Management, Sun Yat-Sen University, Guangzhou, China.
Business School, Hohai University, Nanjing, China.
Digit Health. 2025 Feb 13;11:20552076251314277. doi: 10.1177/20552076251314277. eCollection 2025 Jan-Dec.
WeChat serves as a crucial source of health information, distinguished by its highly personalized nature. Avoidance of such personalized health information has a direct impact on individuals' health decision-making. This study aims to identify the factors influencing personalized health information avoidance on WeChat and to construct a hierarchical framework illustrating the relationships among these factors.
A hybrid method was utilized. Semi-structured interviews and grounded theory were used to identify the influencing factors. The interpretive structural modeling (ISM) method was adopted to develop a hierarchical model of the identified factors, followed by matrice d'impacts croises-multiplication appliqué a un classemen (MICMAC) to analyze the dependence and driving power of each factor.
The 20 predictors of personalized health information avoidance were broadly categorized into three groups: personal, informational, and social factors. These factors collectively form a three-tier explanatory framework, consisting of the top, middle and bottom layers. At the root layer, health characteristics and cognition exerted a strong driving force, while negative emotions and affective factors at the top layer showed a high degree of dependence. In contrast, the decision-making cognition, informational factors, and social factors in the middle layer exhibited relatively weaker driving force and dependence power.
This study bridged the research gap of information avoidance by providing new insights targeting the factors influencing personalized health information avoidance behavior on WeChat. It also contributed to enhancing personal health information management and the health information services provided on WeChat.
微信是健康信息的重要来源，具有高度个性化的特点。避免此类个性化健康信息会直接影响个人的健康决策。本研究旨在确定影响微信上个性化健康信息回避的因素，并构建一个层次框架来说明这些因素之间的关系。
采用了混合方法。通过半结构化访谈和扎根理论来确定影响因素。采用解释结构模型（ISM）方法建立所确定因素的层次模型，随后运用交叉影响矩阵乘法应用于分类分析（MICMAC）来分析每个因素的依赖性和驱动力。
个性化健康信息回避的20个预测因素大致分为三类：个人因素、信息因素和社会因素。这些因素共同构成了一个三层解释框架，包括顶层、中层和底层。在根层，健康特征和认知具有很强的驱动力，而顶层的负面情绪和情感因素表现出高度的依赖性。相比之下，中层的决策认知、信息因素和社会因素表现出相对较弱的驱动力和依赖力。
本研究通过针对影响微信上个性化健康信息回避行为的因素提供新见解，弥补了信息回避研究的空白。它还有助于加强个人健康信息管理以及微信上提供的健康信息服务。
